SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs

@inproceedings{Rus2013SEMILARAS,
  title={SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs},
  author={Vasile Rus and Rajendra Banjade and Mihai C. Lintean and Nobal B. Niraula and Dan Stefanescu},
  booktitle={EDM},
  year={2013}
}
We present in this demo SEMILAR, a SEMantic similarity toolkit. SEMILAR includes offers in one software environment several broad categories of semantic similarity methods: vectorial methods including Latent Semantic Analysis, probabilistic methods such as Latent Dirichlet Allocation, greedy lexical matching methods, optimal lexico-syntactic matching methods based on word-to-word similarities and syntactic dependencies with negation handling, kernel based methods, and some others. We will… CONTINUE READING
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